AI Poisoning

AI poisoning is a type of cyberattack where attackers sneak misleading or harmful data into the training sets of machine learning models. This can be done by adding fake information, changing existing data, or deleting important data points. The goal is to trick the AI into making wrong decisions, such as misclassifying emails or giving biased results.
There are different types of attacks:

• Targeted attacks focus on specific outcomes, like letting certain spam emails through.
• Untargeted attacks aim to reduce the overall accuracy of the model.
• Clean-label attacks use data that looks normal but is subtly altered to fool the AI .

Understanding AI poisoning is crucial because it can silently damage the trustworthiness of AI systems. In critical areas like healthcare, finance, or autonomous vehicles, poisoned data can lead to dangerous mistakes, financial loss, or safety risks. Recognizing and preventing these attacks helps keep AI reliable and secure.

AI poisoning works by manipulating the data that an AI system learns from. For example, an attacker might add incorrect labels to images in a training set, causing the AI to misidentify objects. In business, attackers could poison data to bypass fraud detection or skew financial predictions.
To defend against AI poisoning:

• Use data validation and sanitization tools.
• Monitor for unusual changes in model performance.
• Regularly audit datasets and restrict access to training data .

Imagine a hospital uses AI to help diagnose patients. An attacker secretly adds misleading patient records to the training data. As a result, the AI starts making incorrect diagnoses, putting patients at risk. This shows how AI poisoning can have serious real-world consequences if not detected and prevented.

To counter unauthorized scraping by AI systems, certain artists are deploying data poisoning—intentionally corrupting their work so that any model trained on it generates distorted results.